Title :
Land cover classification by using screening and truncated normal distribution
Author :
Hosomura, Tsukasa
Author_Institution :
Dept. of Inf. & Arts, Tokyo Denki Univ., Saitama, Japan
Abstract :
In this paper, in order to improve the classification accuracy training data screening and truncated normal distribution maximum likelihood classification are adopted. We verify these techniques are effective measures for the improvement in the classification accuracy
Keywords :
geography; image classification; normal distribution; remote sensing; land cover classification; maximum likelihood classification; screening; truncated normal distribution; Art; Classification algorithms; Data mining; Equations; Gaussian distribution; Humans; Probability density function; Probability distribution; Tail; Training data;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-7031-7
DOI :
10.1109/IGARSS.2001.976101
